What Is RAG? Building an Internal Knowledge Assistant

Your team keeps asking the same questions: Where are the installation steps? Which document contains the return policy? What is the latest quotation template? The documents exist, but finding the right one and checking its version takes time. An internal knowledge assistant can help with that search. RAG is one approach to building it.

Our suggested starting point is a measurable pilot using one team’s approved documents. The plan below is an implementation proposal; its timeline and examples are not reported customer results.

What is RAG?

Retrieval-augmented generation (RAG) retrieves information relevant to a question and supplies it to a language model as context. New documents can be made available without retraining the model for each update. The basic flow is to receive a question, retrieve useful passages and generate a response based on them. NIST’s RAG definition describes this distinction.

For example, a support specialist asks what to check before installing a product. The assistant finds the current installation guide within that person’s permissions, summarizes the relevant section and links to the document. Seeing which version supported the answer matters more than polished wording alone.

As Microsoft’s RAG overview explains, retrieval can use keyword, vector or hybrid search. Installing a vector database does not by itself produce a useful assistant. Source quality and relevance still need evaluation.

Choose a task with a verifiable answer

Good initial candidates include product manuals, approved operating procedures and recurring internal support questions. Live inventory, balances and order status should come from the relevant business system. An old PDF should not become the authority for changing operational data.

Need	Starting approach	Acceptance check
Find installation steps	Search approved technical documents	Correct document and version
Explain a procedure	Cite the relevant procedure section	Answer matches the approved steps
Check current order status	Query an authorized business system	Result matches that system
Draft a quotation	Use an approved template with human review	Correct terms and customer details

This also clarifies the boundary between a knowledge assistant and an AI agent: explaining a document and executing a business transaction require different permissions.

Prepare documents before choosing a model

Record the owner, approved version, review date and permitted user groups for each pilot document. If both an old procedure and its replacement are present, state which one is valid. For scanned files, inspect sample pages to check the extracted text.

Uploading everything first makes it harder to understand what the pilot is measuring. Start with a smaller collection whose ownership is clear. Where documents change regularly, define the refresh process and the removal of superseded passages before launch.

Assign someone to handle questions that expose missing or contradictory documentation. An assistant can make those gaps visible; it cannot decide which department’s conflicting procedure is authoritative.

Apply permissions before information reaches the model

An assistant should not reveal a document that its user cannot otherwise access. Enforce permissions before retrieved content reaches the model, and propagate access changes to search passages and caches. Instructions embedded in a document should be treated as content, not as commands to the application. These controls are discussed in the OWASP RAG security guidance.

During product selection, check which services receive company documents, their retention settings and the scope of access logs. Your workplace AI usage policy should cover the assistant too.

Design a two-week pilot around evidence

In our proposed pilot, the opening days establish the scope and document inventory. The team then supplies real questions, with an expected source and acceptable answer for each. Finally, those questions are tested under different user roles.

Include ordinary questions, questions the documents cannot answer, prompts that suggest obsolete instructions and requests for restricted information. A clear refusal when evidence is missing is a useful result.

Track correct-source selection, answer faithfulness, appropriate abstention, response time and query cost separately. A citation does not make an incorrect answer correct. Do not average unauthorized disclosure into a general quality score: it requires a fix to the access design.

Agree on acceptance criteria before comparing systems. A pilot should answer whether the assistant improves a defined task, not simply whether people enjoy trying it.

Budget for the whole workflow

Costs extend beyond model usage. Document cleanup, text extraction, index refreshes, permissions integration, evaluation and maintenance all require work. A useful quotation brief explains not only the number of files, but who maintains them, who may access them and how an incorrect answer will be detected.

Lumethis can help define that scope and the acceptance criteria for a small pilot. Explore our AI and custom software services, or contact us to review your document workflow.

Technical sources checked on November 1, 2026. The pilot workflow is Lumethis’s implementation proposal for this article.